Galaxy number count by using Optical images Supervisor ：川崎涉 (Kawasaki Wataru) 潘國全 (Pan Kuo-Chuan)

Outline I. Galaxy number count and probing the cosmological parameters II.The data we used III.Optical data reduction and calibration IV. Result

Galaxy number count and probing the cosmological parameters

There are many methods to probe the cosmological parameters. Number counting of faint galaxies is one of the most fundamental observational test to determine the cosmological density parameter, because it depends on the geometry of the universe. The spectrum of normal galaxies usually have a peak at optical light, so we used the optical images to count galaxies. Introduction

Introduction to cosmological parameters The Friedmann equations describing the space and time by considering the homogeneous and isotropic universe, which is given as :

We can define some cosmological parameter of Friedmann cosmological models. Hubble constant, Density parameter Deceleration parameter Curvature parameter Λ parameter Picture from :

The galaxy count data are obtained by counting up all images of galaxies on a finite area of the sky. Let n(m λ,z )dm λ dz be the number of galaxies between m λ and m λ + dm λ and between z and z + dz, then we have : Galaxy number count Why this depend on the cosmological parameter?

START Assume Depend on cosmological parameters Ya!

Picture from: The Data we used Subaru

Instruments of Subaru Telescope Infrared Camera and Spectrograph (IRCS) Coronagraphic Imager with Adaptive Optics (CIAO) Cooled Mid Infrared Camera and Spectrometer (COMICS) Faint Object Camera And Spectrograph (FOCAS) Subaru Prime Focus Camera (Suprime-Cam) High Dispersion Spectrograpgh (HDS) OH Airglow Suppression Spectrograph (OHS) Adaptive Optics (AO)

Subaru Prime Focus Camera (Suprime-Cam) Suprime-Cam is one instrument of Subaru Telescope which has 10 CCDs, and each have 2048*4096pixels Picture from: The field of view is 34’x27’ Very unique instrument

Picture from:

Data information Obs. Date : Apr Observed Region Passband : i ’ (SDSS) Exposure time: 6x6min α ： 10h56m45s δ ： -3°37’46”

Optical Data Reduction and Calibration

Picture from:  It has 2048*4096 pixels in each CCD and we take it as a matrix then we say that the photon count of the Pixel ij is I ij. Idea of getting the true objects value

I ij = ( O ij + S ij ) x E ij +B ij I ij = The observed value O ij = The object value S ij = The sky light value E ij = Efficiency B ij =Bias of CCDs So, the object value = ( (I ij -B ij )/ E ij ) - S ij But the value we want is not the raw CCD pixels’ count, it depends on many conditions.

Estimate the Efficiency If we give the same intensity for each pixels.

After subtracting the sky light we can combine the 10CCDs images to be one image

Calibration From now, we only have the electron counts of each pixel, but we need the magnitude of each object. we get the relationship between apparent magnitude and electron count of objects using the standard star : Make a catalog of all the objects in CCD image

Separating the stars and galaxies Peak flux/area

But the weather is bad for different exposures, so we also choose one exposure image to do the number count Peak flux/area

Result: Galaxy number count

Galaxy number counts

Compare with the theoretical number count lines

Main reason Weather changed in different exposure so there have some systematic error. Problem The seeing of data is bad. We can not separate the faint galaxies and stars. For different exposure time the data is not consistent with theoretical line.

Galaxy number count is one way to probe the cosmological parameters. Summary We used some optical image data from Suprime-Cam on Subaru Telescope. With some optical data reduction we get the galaxy number count and consistent with the theoretical model, but not enough deep due to poor quality data. If we had better seeing, we could reach to deeper magnitude to fix the cosmological density parameter.

References I.Galactic Evolution and Cosmology : Probing the Cosmological Deceleration Parameter (ApJ 326:1 1988) II. Unavoidable selection effects in the analysis of faint galaxies in the Hubble deep field (ApJ540: ) III.